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In computer engineering, instruction pipelining is a technique for implementing instruction-level parallelism within a single processor. Pipelining attempts to keep every part of the processor busy with some instruction by dividing incoming instructions into a series of sequential steps (the eponymous "pipeline") performed by different processor units with different parts of instructions ...
The instruction fetch and decode stages send the second instruction one cycle after the first. They flow down the pipeline as shown in this diagram: In a naive pipeline, without hazard consideration, the data hazard progresses as follows: In cycle 3, the SUB instruction calculates the new value for r10.
When a next-line predictor points to aligned groups of 2, 4, or 8 instructions, the branch target will usually not be the first instruction fetched, and so the initial instructions fetched are wasted. Assuming for simplicity, a uniform distribution of branch targets, 0.5, 1.5, and 3.5 instructions fetched are discarded, respectively.
Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically, ILP refers to the average number of instructions run per step of this parallel execution.
In the "Simple superscalar pipeline" figure, fetching two instructions at the same time is superscaling, and fetching the next two before the first pair has been written back is pipelining. The superscalar technique is traditionally associated with several identifying characteristics (within a given CPU):
The instruction cycle (also known as the fetch–decode–execute cycle, or simply the fetch–execute cycle) is the cycle that the central processing unit (CPU) follows from boot-up until the computer has shut down in order to process instructions. It is composed of three main stages: the fetch stage, the decode stage, and the execute stage.
In computer science, instruction scheduling is a compiler optimization used to improve instruction-level parallelism, which improves performance on machines with instruction pipelines. Put more simply, it tries to do the following without changing the meaning of the code:
Pipelining cannot decrease the processing time required for a single task. The advantage of pipelining is that it increases the throughput of the system when processing a stream of tasks. Applying too many pipelined functions can lead to increased latency - that is, the time required for a single task to propagate through the full pipe is ...